A Five-Dimensional Comprehensive Evaluation of the Yellow River Basin’s Water Environment Using Entropy–Catastrophe Progression Method: Implications for Differentiated Governance Strategies

Abstract

The systematic evaluation of the water environment in the Yellow River basin is a critical scientific basis for achieving the goals of ecological protection and high-quality development. In this study, a five-dimensional comprehensive evaluation framework (“quality–quantity–space–flow–biota”) consisting of 19 indicators was constructed. The entropy method and the catastrophe progression method were innovatively combined to solve the limitations of traditional evaluation models in characterizing the nonlinear relationships within water environment systems. The results indicated that the Yellow River basin’s overall comprehensive water environment index was 0.032, classified as “good”. However, the spatial differentiation is significant, showing a step-by-step degradation characteristic of “upstream > downstream > midstream”. Gansu Province (0.028), Ningxia Hui Autonomous Region (0.026), Shaanxi (0.024), and Shanxi (0.020) were rated as “poor” and urgently need to be regulated. The core problems are water shortage (Gansu, Ningxia), water quality deterioration (Shaanxi, Shanxi), and fragmentation of aquatic space (Shanxi, Shaanxi). The findings of this study provided a quantitative tool for differentiated governance in the Yellow River basin which could directly support the decision-making needs of “zoning control and precise policy implementation” in the “Outline of the Plan for Ecological Protection and High-quality Development of the Yellow River Basin”.

1. Introduction

As China’s mother river, the Yellow River is an ecological corridor connecting the Qinghai–Tibet Plateau, the Loess Plateau, and the North China Plain [1]. The Yellow River basin is a key economic belt in China and a green barrier for ecological security. It plays an essential strategic role in China’s ecological landscape [2]. With the rapid development of society and economy, the population in the Yellow River basin has increased dramatically, exacerbating the conflict between ecological conservation and resource exploitation [3]. The region faces severe challenges, including severe water shortage and overexploitation [4], soil erosion, water quality deterioration, water ecosystem degradation and declining service functions [5], and persistent ecological quality issues. These problems are difficult to fundamentally improve and seriously hinder the sustainable development of the Yellow River basin.

Research on water environment assessment in the Yellow River basin has attracted widespread attention from the academic community. This study is based on a review of recent literature and analyzes the current state of research in this field. Most existing studies focus on evaluating a single dimension of the water environment to represent its overall condition [2]. For example, water scarcity: As one of the world’s most water-stressed regions [6], Ma Jun et al. [7] explored the spatial heterogeneity of water consumption in the Yellow River basin from the perspective of virtual water trading and sought to alleviate the water crisis in the Yellow River basin through appropriate water resource allocation and regulation. Qin et al. [8] analyzed the impact of changes in the Yellow River runoff on the water environment in the Yellow River estuary and Laizhou Bay. Water quality: River water quality assessment is a critical aspect of evaluating the water environment status of the river basin. Studies have used water quality monitoring data and surface water environmental quality standards to conduct a comprehensive evaluation of the water quality of the monitored sections of the Yellow River basin using the grayscale method [9]. Hou et al. [10] applied the water quality index method to evaluate the reservoir water quality in the Yellow River’s lower reaches. They found that mercury and sulfur dioxide were the main pollutants in the study area. In addition, as the primary source of agricultural irrigation water in the basin, research by Sun et al. [11] showed that the water quality of the Yellow River irrigation water is of great significance to crop growth and food safety. Sediment dynamics: The Yellow River is the world’s most sediment-laden river system [2], making sediment research a hotspot. For instance, Kong et al. [12] analyzed the full-flow erosion pattern in the lower reaches of the Yellow River under the cement-sand regulation scheme. Their study found that the erosion state is affected by the water level and the corresponding sediment concentration. Ma et al. [13] investigated the impact of human activities on changes in river sediment content in the Three-River-Source National Nature Reserve, while Gao et al. [14] explored the relationship between the Yellow River sediment flux and land change rate in the Yangtze River Delta based on remote sensing images and historical hydrological data. Soil and water conservation: Soil and water conservation is a priority for ecological protection at the basin level. Studies have shown that vegetation restoration can effectively reduce soil erosion [15]. Liu et al. [16] analyzed the dynamic vegetation coverage changes in the Yellow River basin and predicted the future trend of vegetation cover changes in the basin. Other studies have analyzed the changes in vegetation coverage in the Yellow River basin since the launch of the Grain-to-Green Plan in 1999 and the impact of climate and topographic factors on these changes [17]. Wei et al. [18] analyzed the spatiotemporal changes in vegetation coverage in the Yellow River Delta based on remote sensing data. Comprehensive assessments: A series of studies have been conducted on the water environment of the Yellow River basin through analysis of aquatic biodiversity in the basin [19], investigation of river network connectivity in the basin [20], the impact of climate factors on the basin’s ecological environment [21,22], and research on flood and drought prevention and control [23]. Some studies have attempted to evaluate the water environment in the Yellow River Basin by analyzing multiple factors. For example, Zhao et al. [24] and Wang et al. [25] used a systematic dynamic model (“driving force–pressure–state–impact–response”) that incorporated the natural environment, social economy, and other major factors of the basin area to evaluate the comprehensive development level of the basin. Some studies [26] used data envelopment analysis tools (SBM model) to explore the ecological efficiency of different regions and the “water–energy–grain” system in the Yellow River basin [27], as well as the correlation between human activities and ecological security [28]. Although these studies have analyzed the environment from multiple dimensions, several limitations remain: (1) The ecological indicator system lacks integrity. For example, Zhao et al. [24] mainly focused on socio-economic driving mechanisms, with only three of the twelve indicators involving ecological attributes. Similarly, Gu et al. [27] fail to quantify parameters related to aquatic spatial structure. (2) Dynamic weighting and nonlinear analysis among indicators remain unaddressed, hindering the characterization of dynamic competitive relationships between indicators. Moreover, existing approaches assume gradual system evolution, neglecting potential abrupt changes triggered by specific indicators and threshold effects on system stability. For instance, system dynamics models [25] and SBM models [26] rely on fixed weight assignments and linear equilibrium assumptions. Given the complex system composition of the Yellow River Basin, it is urgent to explore an appropriate evaluation method to evaluate the water environment comprehensively.

With society’s rapid development, human use of water resources is not limited to consumption but also includes hydropower generation, water area encroachment, waste disposal, etc. [29,30,31]. Any over-exploitation of water resources will cause the imbalance and collapse of the entire water environment system [32]. Based on the attributes of the water environment system, this study divides the water environment into five dimensions: water quantity, water quality, aquatic space, water flow, and aquatic biota.

Water quality: River water quality assessment is the core to traditional water resource protection. Water quality deterioration has become a global concern [33], as it is closely related to socioeconomic development, human health, and ecological balance. Uncontrolled water quality degradation will inevitably harm societal development and human health.

Water quantity: The Yellow River basin faces serious ecological challenges, including water shortages and soil erosion [12]. Water quantity assessment plays a vital role in regulating surface runoff, promoting nutrient cycling, and increasing available water resources [34], and is therefore crucial for water resource management in the basin.

Aquatic space: Rapid societal development has led to habitat fragmentation and the shrinkage of aquatic spaces in 50% of global rivers [35]. Maintaining the structural integrity and functional stability of aquatic spaces is the key to ecological balance. Therefore, strictly delineating the protection boundaries of aquatic space and strengthening the management of aquatic space structure and functions are essential prerequisites for ecological stability [31].

Water flow: Rivers are complex, dynamic systems whose morphology, structure, and connectivity affect the flow and circulation of sediments and other materials. Understanding river systems can help optimize river network distribution, improve regional disaster resilience, and enhance water resource allocation [36]. Therefore, river connectivity assessment is an indispensable part of comprehensive water environment research.

Aquatic biota: Aquatic organisms are vital components of aquatic ecosystems, forming complex food chains and ecological networks [37]. Their interactions maintain the stability and health of aquatic ecosystems, making them important indicators of aquatic ecological conditions.

In view of the previous research results mentioned above, this study constructed a five-dimensional comprehensive evaluation framework of the water environment, “quality–quantity–space–flow–biota”, based on the system attributes of the water environment. By analyzing the multifaceted impacts of human activities on the water environment, we used the entropy method to dynamically assign weights, capture the competitive relationships between indicators, and combine the catastrophe progression method to identify critical thresholds. This approach achieved a methodological transition from linear equilibrium analysis to nonlinear mutation warning, and provided a dynamical and adaptive evaluation tool for the management of complex human–water systems. Compared with existing basin-scale water environment evaluation studies, this framework showed obvious advantages in ecological integrity, dynamic weighting, and nonlinear analysis. Comprehensive evaluation of the impact of human activities on the water environment of the Yellow River basin is of great significance to promoting ecological protection and high-quality development of the Yellow River basin.

2. Research Materials

Study Area

The Yellow River basin (China) originates from the Bayan Har Mountains (China), flows into the Bohai Sea, and runs from west to east through nine provincial administrative regions, including Qinghai, Sichuan, Gansu, Ningxia, Shaanxi, Shanxi, Inner Mongolia, Henan, and Shandong. The basin is about 1900 km long from east to west and about 1100 km wide from north to south, with a total area of 795,000 km² (Figure 1). The population of the Yellow River basin accounts for one-third of China’s total population, and its GDP accounts for about one-quarter of the nation’s total. The basin is rich in natural resources, especially coal and petroleum, and is an important energy and chemical production base.

The climate in the Yellow River Basin varies greatly, with a temperature difference of about 25 °C between east and west and about 10 °C between north and south. These variations result in significant differences in annual precipitation and seasonal temperature fluctuations. However, most areas in the basin are arid or semi-arid, and water resources are generally scarce. In recent years, with rapid economic and social development, water resource issues have become increasingly prominent, water pollution has become increasingly serious, downstream flow interruptions have occurred frequently, and land desertification and wetland reduction have intensified. These water environment issues have become key constraints to the sustainable social and economic development of the provinces within the Yellow River basin.

3. Research Methods

3.1. Construction of the Comprehensive Water Environment Evaluation System

Based on the connotation of comprehensive water environment evaluation, this study selected 19 indicators from five dimensions of “quantity–quality–space–flow–biota” to construct a comprehensive water environment evaluation system, as shown in

Table 1. Comprehensive evaluation indicator system for water resource synergistic protection.

Dimension	Evaluation Indicator	Indicator Description
Water Quantity (V)	Precipitation (V1)	Average annual precipitation in the region
	Per Capita Water Resources (V2)	Available water resources per capita in the region
	Proportion of Water-Saving Irrigation Area (V3)	Area of water-saving irrigated farmland/Total area
	Proportion of Newly Added Soil Erosion Control Area (V4)	Newly added soil erosion control area/Total area
	Proportion of Grassland, Forest, and Wetland Area (V5)	Area of grassland, forest, and wetland/Total area
Water Quality (Q)	Urban Sewage Treatment Rate (Q1)	Urban sewage generated/Urban sewage treated by treatment plants
	Number of Industrial Wastewater Treatment Facilities (Q2)	Number of industrial wastewater treatment facilities in the region
	Proportion of Environmental Pollution Control Investment (Q3)	Investment in pollution control/GDP
	Proportion of Class I–III Water Quality Monitoring Stations (Q4)	Number of Class I–III water quality monitoring stations/Total number of monitoring stations
Aquatic Space (W)	Rate of Change in Aquatic Area (W1)	Change in aquatic area/Initial aquatic area
	Retention Rate of Aquatic Area (W2)	Retained aquatic area/Initial aquatic area
	Protection Rate of Aquatic Space (W3)	Protected aquatic area/Total aquatic area
	Aggregation Degree of Aquatic Space (W4)	Adjacent aquatic area/Largest patch area
	Largest Patch Index (W5)	Area of the largest connected patch/Total aquatic area
Water Flow (L)	α Index (L1)	Reflects the actual loop level of the river network
	β Index (L2)	Reflects the connectivity strength of each node in the river network
	γ Index (L3)	Reflects the connectivity strength between river network corridors
Aquatic Biota (O)	Freshwater Aquaculture Production (O1)	Production of freshwater aquatic organisms through aquaculture
	Freshwater Fishing Production (O2)	Production of freshwater aquatic organisms through fishing

. The selected indicators are intended to comprehensively and scientifically reflect the impact of human activities on various elements of the protection or degradation of the water environment.

3.2. Calculation of the Comprehensive Evaluation Index

This study used the entropy–catastrophe progression method to calculate the comprehensive evaluation index of water environment of the Yellow River basin. A two-stage weight allocation strategy is adopted to ensure the independence of indicator weights within each dimension and the systematic coordination of weights between dimensions. The entropy method, based on the principle of information entropy, establishes an evaluation matrix to determine the weights of evaluation indicators in the comprehensive process. This method is a comprehensive evaluation and decision-making approach for multi-indicator and multi-objective systems. The entropy value indicates the competition intensity between evaluation indicators, which significantly reduces the subjective influences and ensure strong objectivity. The specific calculation process is as follows:

Construct the judgment matrix Z for i samples and j evaluation indicators:

$$Z=\left[\begin{array}{ccc}{x}_{11}& \cdots & x_{1j}\\ ⋮& \ddots & ⋮\\ x_{i1}& \cdots & x_{ij}\end{array}\right]$$

(1)

Standardize the data to obtain the new judgment matrix R:

$$r_{qp}={\displaystyle \frac{X_{qp}-X_{p min}}{X_{p max}-X_{p min}}}$$

(2)

$$r_{qp}={\displaystyle \frac{X_{p max}-X_{qp}}{X_{p max}-X_{p min}}}$$

(3)

$$R={\left(r_{qp}\right)}_{i\times j}$$

(4)

where X_qp represents the actual value of evaluation object p for indicator q, X_{p min} is the minimum value of evaluation object p, X_{p max} is the maximum value of evaluation object p, and r_qp represents the standardized value of X_qp. For positively oriented indicators, Equation (2) is selected for standardization, while Equation (3) is applied to negatively oriented indicators. This process ultimately yields the standardized matrix R.

Determine the information entropy of each indicator:

$$H_q={\displaystyle \frac{1}{\ln \left({{\displaystyle \sum }}_{q=1}^i{f}_{qp}ln{f}_{qp}\right)}}$$

(5)

$$f_{qp}={\displaystyle \frac{1-H_q}{{{\displaystyle \sum }}_{q=1}{r}_{qp}}}$$

(6)

where H_q is the information entropy of indicator q and f_qp is the characteristic proportion.

Calculate the weight of each indicator based on information entropy:

$$W_q={\displaystyle \frac{1-H_q}{j-{{\displaystyle \sum }}_{q=1}^j{H}_q}}$$

(7)

where W_q is the weight coefficient of evaluation indicator q, satisfying ${\displaystyle \sum }_{q=1}^j{W}_q$=1.

3.3. Catastrophe Progression Method

The catastrophe progression method primarily examines how system status undergoes discontinuous transitions—evolving from gradual quantitative changes to abrupt qualitative shifts—under parameter variations [32]. In comprehensive water environment evaluation, when certain indicators reach critical thresholds, it may lead to imbalance of entire aquatic ecosystem. Thus, changes in the ecological security of aquatic ecosystems under multiple indicators can be regarded as a catastrophe process. Based on the number of control variables, catastrophe models can be categorized into four types: cusp, swallowtail, butterfly, and wigwam models, as shown in

Table 2. Relationships between control variables and state variables in common catastrophe models.

Type	Mathematical Expression	Bifurcation Set Equation	Normalization Equation
Cusp Catastrophe	$f\left(x\right)=x^4+a{x}^2+bx$	$a=-6x^2, b=8x^3$	$x_a=a^{{\displaystyle \frac{1}{2}}},x_b=b^{{\displaystyle \frac{1}{3}}}$
Swallowtail Catastrophe	$f\left(x\right)=x^5+a{x}^3+b{x}^2+cx$	$a=-6x^2, b=8x^3,c=-3x^4$	$x_a=a^{{\displaystyle \frac{1}{2}}},x_b=b^{{\displaystyle \frac{1}{3}}},x_c=c^{{\displaystyle \frac{1}{4}}},$
Butterfly Catastrophe	$f\left(x\right)=x^6+a{x}^4+b{x}^3+c{x}^2+dx$	$a=-10x^2, b=20x^3,$  $c=-15x^4,d=4x^5$	$x_a=a^{{\displaystyle \frac{1}{2}}},x_b=b^{{\displaystyle \frac{1}{3}}},x_c=c^{{\displaystyle \frac{1}{4}}},x_d=d^{{\displaystyle \frac{1}{5}}}$
Wigwam Catastrophe	$f\left(x\right)=x^7+a{x}^5+b{x}^4+c{x}^3+d{x}^2+ex$	$a=-x^2, b=2x^3,c=-2x^4,$  $d=4x^5,e=-5x^6$	$x_a=a^{{\displaystyle \frac{1}{2}}},x_b=b^{{\displaystyle \frac{1}{3}}},x_c=c^{{\displaystyle \frac{1}{4}}},$  $x_d=d^{{\displaystyle \frac{1}{5}}},x_e=e^{{\displaystyle \frac{1}{6}}}$

.

3.4. Natural Breaks Classification Method

This study used the natural breaks method to classify the comprehensive evaluation results of the water environment. The method is based on the Jenks optimization algorithm, which identifies the natural grouping structure inherent in data by minimizing intra-class variance and maximizing inter-class variance. It has the characteristics of adaptive data distribution and objective classification and is highly applicable to evaluating ecological indicators.

3.5. Data Sources

This study utilized four types of data: statistical data, measured data, remote sensing data, and fishery resource data. Socioeconomic data were sourced from the 2022 China Statistical Yearbook; environmental development data were obtained from the 2022 China Environmental Statistical Yearbook and the 2022 Water Resources Bulletin; fishery resource data were derived from the China Fishery Statistical Yearbook; water environment monitoring data were collected from the China Water Resources Information System real-time monitoring website https://www.cnemc.cn/sssj/szzdjczb/ (accessed on 21 December 2024); and land use data were obtained from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences https://www.resdc.cn/ (accessed on 21 December 2024).

4. Research Results

4.1. Comprehensive Evaluation Index Calculation Based on the Entropy–Catastrophe Progression Method

In this study, the entropy–catastrophe progression method was used to construct a comprehensive t evaluation index of water environment in the Yellow River basin. The calculation process consisted of three stages: (1) Data Standardization and Intra-Dimensional Weighting: Dimensional differences were eliminated through range standardization, and dynamic weights for indicators within each dimension were computed using the entropy method (Figure 2); (2) Nonlinear Inter-Dimensional Weight Allocation: The bifurcation set equations of catastrophe models were utilized to transform standardized dimension scores into normalized weights (Water Quality: 0.19; Water Quantity: 0.27; Aquatic Space: 0.23; Water Flow: 0.12; Aquatic Biota: 0.19), capturing critical threshold effects; (3) Composite Index Synthesis: The three-level coupling of “indicator-dimension-system” was realized through weighted superposition, and then a comprehensive evaluation result with significant spatial heterogeneity was generated.

As shown in Figure 2, the water quality dimension (Q) had the highest weight for water quality of Class I–III (Q3 = 0.4919), indicating that it was the core driving factor of water quality evaluation. In the aquatic space dimension (W), the aggregation degree of aquatic space (W4 = 0.3732) exhibited significant weighting, highlighting the critical importance of spatial integrity to ecological functionality. The water quality score was dominated by Q3, while aquatic space fragmentation (W4 = 0.009) was significantly elevated in the aquatic space dimension. This method combined dynamic weighting with nonlinear mapping, effectively solving the limitations of traditional models in threshold recognition and spatial adaptability, and providing high-precision decision-making support for differentiated management of the Yellow River basin.

4.2. Evaluation Results of Each Water Environment Dimension

In terms of quality, Qinghai Province had the best quality, while Shanxi Province had the worst quality, with Class I–III concentrations at 90% and 72% respectively,. The provinces with the largest number of industrial wastewater treatment facilities were Shandong, Sichuan, and Henan. As shown in Figure 2, the province with the best water quality was Qinghai Province (Q = 0.21). The overall water quality was ranked from good to bad as follows: upstream > downstream > midstream. The water quality score of the midstream provinces (Shaanxi and Inner Mongolia) (Q = 0.07) was significantly lower than that of the downstream, which might be directly related to the pollutant emissions in the coal industry-intensive areas in the midstream (such as Ordos and Yulin).

Water Quantity Dimension: Qinghai Province had the highest per capita water volume, 12,206.9 m³, while Ningxia (122.5 m³) and Henan Province (252.5 m³) were experiencing severe water shortages. The provinces with the largest proportion of water-saving irrigation area were Shandong (21.9%) and Henan (13.2%). As shown in Figure 2, the provinces with good water quantity were Qinghai Province (V = 0.14) and Sichuan Province (V = 0.14), while the provinces with poor water quantity were Gansu Province (V = 0.058) and Inner Mongolia Autonomous Region (V = 0.063). These results were consistent with the precipitation in the Yellow River basin. The average annual precipitation in the upper and lower reaches was 500 mm and 200 mm respectively, showing a pattern of dryness in the west and wetness in the east.

Aquatic Space Dimension. Shandong Province had the highest aquatic spatial aggregation (0.064), while Shaanxi Province (0.009) and Gansu Province (0.008) had the lowest. The aquatic space protection rate in Qinghai and Gansu provinces was relatively high, both reaching more than 30%. The provinces with the largest proportion of water-saving irrigation area were Shandong (21.9%) and Henan (13.2%). As shown in Figure 2, the provinces with good water area dimensions were Qinghai Province (W = 0.21) and Shandong Province (W = 0.20), while the provinces with poor water area dimensions were Shanxi Province (W = 0.02) and Ningxia Province (W = 0.04). The water area in the middle reaches was significantly worse than that in the upper and lower reaches. In recent years, urbanization had squeezed the water area in the middle reaches, causing serious aquatic area fragmentation, resulting in the rupture of ecological corridors and weakening soil and water conservation capacity.

Water Flow Dimension. Inner Mongolia Autonomous Region had the highest coefficients of three flow indices, while Sichuan Province had the lowest. As shown in Figure 2, the provinces with better water flow dimension indices were Inner Mongolia Autonomous Region (L = 0.36) and Qinghai Province (L = 0.23), while Sichuan Province (L = 0.01) and Henan Province (L = 0.02) were relatively poor. In Henan Province, cascade hydropower stations (such as Xiaolangdi) and irrigation water diversion had led to a near-break in connections between rivers, hindering fish migration and reducing the connectivity of aquatic habitats. These observations confirmed previous research [20] that dams hindered fish migration and that Sichuan Province was generally less affected due to the shorter Yellow River in its territory.

Aquatic Biota Dimension. Henan Province had the most freshwater fish fishing, Sichuan Province had the most freshwater fish farming, and Gansu Province had the lowest numbers for both. As shown in Figure 2, Shandong Province (O = 0.330), Henan Province (O = 0.329), and Sichuan Province (O = 0.254) had higher aquatic biota scores compared with other provinces. The shrinkage and pollution of aquatic habitats in Gansu Province (O = 0.000) had led to a sharp decline in the number of native fish populations (such as Coreius septentrionalis). Despite being located in the Sanjiangyuan Nature Reserve, the degradation of alpine wetlands had slowed the recovery of cold-water fish populations (such as Gymnocypris przewalskii) in Qinghai Province (O = 0.008).

4.3. Comprehensive Evaluation Results of the Water Environment

Based on the evaluation results of each water environment dimension, the entropy–catastrophe progression method was used to calculate the comprehensive water environment index of each province in the Yellow River basin in 2022. The comprehensive status of each province’s water environment was evaluated using the natural breaks method, and the results were shown in the corresponding

Table 3. Comprehensive evaluation scores of water environment in provinces of the Yellow River basin.

Province	Water Environment Evaluation of All Dimensions					Comprehensive Evaluation
	Water Quality	Water Quantity	Aquatic Space	Aquatic Biota	Water Flow
Shanxi	0.090	0.117	0.078	0.004	0.022	0.020	poor
Neimenggu	0.070	0.063	0.361	0.030	0.097	0.030	good
Shandong	0.132	0.131	0.073	0.330	0.199	0.049	excellent
Henan	0.113	0.086	0.023	0.330	0.125	0.037	good
Sichuan	0.112	0.140	0.001	0.254	0.082	0.035	good
Shaanxi	0.073	0.124	0.109	0.026	0.059	0.024	poor
Gansu	0.107	0.059	0.098	0.001	0.126	0.028	poor
Qinghai	0.200	0.140	0.230	0.008	0.209	0.041	excellent
Ningxia	0.106	0.071	0.215	0.029	0.043	0.026	poor
Total	0.112	0.104	0.132	0.112	0.107	0.032	good

and Figure 3. The comprehensive evaluation score of the water environment in the Yellow River basin in this study was 0.032, which is considered “good”. Overall, the effect of water environment management was significant, but further protection and management are still needed. The provinces with poor comprehensive water environment evaluation scores included Ningxia Hui Autonomous Region, Shaanxi, Shanxi, which are located in the middle reaches of the Yellow River, and Gansu Province, which is located in the upper reaches.

The two provinces with the worst comprehensive water environment scores were Shaanxi and Shanxi. The two provinces faced similar water environment challenges, with low scores of water quality and aquatic space dimension. Regarding water quality, the two provinces had serious soil erosion, and the risks of water and solid waste pollution during the flood season were prominent. Their water quality was lower than the average level of the basin. In terms of aquatic space, the intensity of societal development has significantly disrupted aquatic spaces, leading to substantial changes in aquatic space structure and function. From the perspective of aquatic space, societal development in the two provinces had caused considerable damage to the water spaces, and their structure and function had undergone substantial changes. Ningxia Hui Autonomous Region and Gansu Province also had relatively poor comprehensive scores, performing poorly in the water quantity dimension. Most of these two provinces were arid or semi-arid areas with low precipitation and per capita water availability. Rapid industrialization and urban population growth have exacerbated water resource shortages, and soil and water conservation needs to be strengthened. These regions remain the primary sediment sources in the upstream of the Yellow River Basin. Additionally, Ningxia Hui Autonomous Region also scored poorly in the aquatic space dimension.

4.4. Issues and Governance Recommendations for the Water Environment in the Yellow River Basin

This study evaluates the comprehensive status of the water environment in various provinces in the Yellow River basin from the comprehensive perspective of the “quality–quantity–space–flow–biota” network. The results indicate that the water environment conditions in the upstream and downstream regions of the Yellow River Basin were better than those in the midstream region. Among the nine provinces through which the Yellow River flows, the overall comprehensive water environment status in Gansu Province, Ningxia Hui Autonomous Region, Shaanxi Province, and Shanxi Province was relatively poor. The water environment in these provinces needs to be continuously monitored and properly managed. The five dimensions of the water environment (“quality–quantity–space–flow–biota”) in these regions varied and required different governance priorities.

4.4.1. Aquatic Space Dimension

Shaanxi Province, Shanxi Province, and Ningxia Hui Autonomous Region scored relatively low in the aquatic space dimension and were key areas for water environment governance. Rapid urbanization and land overexploitation had led to the shrinkage of regional aquatic spaces, severe landscape fragmentation [38,39], and land use conflicts [40]. The semi-arid climate and human activities had also increased ecosystem vulnerability and the risk of ecosystem degradation in these areas [5]. To address these issues, the following measures are recommended: (1) Optimize land use structures to promote land conservation and efficient utilization. (2) Strengthen spatial management of river and lake shorelines, define management boundaries based on regional characteristics, and expand water resource protection areas and ecological buffer zones to mitigate human disturbances. (3) Enhance ecological restoration of aquatic shorelines, construct ecological corridors along rivers and lakes based on natural aquatic morphology, optimize landscape layouts, and coordinate the development of regional natural landscapes.

4.4.2. Water Quality Dimension

Special attention should be paid to water quality issues in Shaanxi and Shanxi provinces in the middle reaches of the Yellow River. The social and economic activities in the Yellow River Basin mainly take place in the middle and lower reaches. More than 70% of the population lives in these areas, and the industry is relatively developed [41]. As a result, domestic and industrial pollution are the main sources of pollution in Shaanxi and Shanxi provinces Additionally, agriculture and animal husbandry are other major sources of pollution, primarily in the form of non-point source pollution. The extensive use of fertilizers and pesticides in agricultural practices, combined with irrigation return flows, increases the organic pollution load in the Yellow River [42]. Both Shaanxi and Shanxi Province are facing water scarcity. Agricultural water largely comes from water diversion projects such as the “Yellow River Diversion to Shanxi” and “Yellow River Diversion to Shaanxi”, which further exacerbate water quality deterioration. The following measures are recommended to address these issues: (1) Improve regional sewage collection systems to prevent domestic sewage from being discharged directly into rivers or seeping into soil and entering rivers through surface runoff. (2) Establish integrated waste collection facilities to prevent leachate from accumulating domestic waste from polluting water bodies [43]. (3) Introduce advanced agricultural practices, such as efficient irrigation technologies, environmentally friendly fertilizers, and pesticides.

4.4.3. Water Quantity Dimension

Gansu Province and Ningxia Hui Autonomous Region, located in the upstream region, need to pay special attention to the water quantity dimension. The water resources in these areas are significantly impacted by climate change. Climate change significantly affects the water resources in these areas. The upper reaches of the Yellow River are located on the Qinghai–Tibet Plateau and the Mongolian Plateau, far from the ocean and with low air humidity [3]. However, glacial meltwater in the upstream region makes up for the lack of precipitation. Currently, overgrazing in the source region of the Yellow River has led to grassland degradation and wetland shrinkage, which has directly reduced the water conservation capacity [44]. In addition, the increase in urban water use and agricultural irrigation have exacerbated water shortage problem in Gansu and Ningxia, and the per capita water resources are far below the national average. To address these issues, the following measures are recommended: (1) Promote water-saving irrigation in water-scarce areas, strengthen field water conservation, and implement precision agricultural water management. (2) Develop water-efficient cities, accelerate the construction and renovation of rural water supply facilities, and support pipeline networks. (3) Strictly control the increase in water use in high water-consuming industries, and promote industrial water-saving reforms.

5. Discussion

The water environment issues in the Yellow River basin are characterized by complexity, systematicity, and dynamic evolution. The traditional single-dimensional or simplified indicator system is difficult to fully reflect the multidimensional contradiction between ecological security and sustainable development. This study is based on 19 indicators in the five-dimensional framework of “quality–quantity–space–flow–biota” and combined with the entropy–catastrophe progression method to reveal the comprehensive state and regional heterogeneity of the water environment system in the Yellow River basin. Compared with existing research, the advantages and necessity of this study are reflected in the following aspects:

• Systemic Advantages of the Multidimensional Framework in Aquatic Ecological Evaluation

Conventional studies on the water environment in the Yellow River basin tend to focus on single dimensions or simplified indicators (e.g., water quality, runoff, and sediment), which have in-depth insights into specific areas but fail to uncover the synergistic effects of the system. For example, Fu et al. [9] used the grey clustering method to evaluate the water quality of 12 monitoring sections in the basin and concluded that water quality of the basin was “generally good”, but this conclusion only reflected the compliance with chemical standards. Our study found that the proportion of Class I–III water quality in Shanxi Province (72%) had improved significantly compared to historical levels, but the scores of aquatic biota (O = 0.004) and aquatic space (W = 0.022) dimensions ranked lowest in the basin, indicating that there is a significant deviation between traditional water quality evaluation and ecological health. Similarly, Qin et al. [8] used the FVCOM model to analyze the impact of runoff on estuarine salinity dispersion, but overlooked the interference of human factors on water–sediment balance. Although Ma et al. [13] established a runoff–sediment–precipitation response model, their analysis of human intervention measures (such as soil and water conservation projects) was still limited to correlation studies.

The water evaluation system of this study not only incorporates natural factors (such as precipitation and vegetation coverage), but also innovatively incorporates human activity indicators (such as water-saving irrigation area ratio and per capita water resources). This approach reveals the dual dilemma of “insufficient water conservation through engineering and severe water shortage in resources” that is common in the mid-upper provinces, such as Gansu (V = 0.058) and Ningxia (V = 0.071), except Qinghai. Although existing studies (e.g., Yujun et al. [20]) emphasize that dam construction leads to reduced connectivity across the entire basin, they lack regional specificity. Our streamflow evaluation system quantifies the structural integrity of the river network through the α, β, and γ indices, identifying significant spatial heterogeneity in the midstream provinces. For instance, Henan Province (L = 0.06) scored significantly lower than upstream regions such as Inner Mongolia (L = 0.36). Existing studies [15,16] utilized vegetation coverage indicators (such as NDVI) to reveal the spatial disparities between upstream arid areas (such as Inner Mongolia, Ningxia, and northern Gansu, with average annual FVC < 0.3) and downstream humid provinces (such as Henan and Shandong, with an average annual FVC > 0.6); however, their research framework ignored aquatic spatial structure metrics. This study shows that despite the midstream provinces have restored vegetation through initiatives such as returning farmland to forest, their aquatic space scores (W = 0.06–0.09) remain significantly lower than those of the upstream Qinghai Province (W = 0.21) and downstream Shandong Province (W = 0.20). Key issues include low aquatic space aggregation, reduced maximum patchiness indices, and aquatic landscapes fragmentation, which weaken ecological corridor functions and offset the benefits of vegetation restoration. These findings expose the compound challenges facing the mid-stream region: “water quality improvement, severs water shortage, and fragmented aquatic spaces”.

2.

Enhanced Dynamicity and Objectivity via the Entropy–Catastrophe Progression Method

Traditional water environment evaluation methods (e.g., WQI, DPSIR models, principal component analysis) usually rely on static weights or subjective assignments, failing to capture nonlinear dynamic features. The entropy–catastrophe progression method addresses these limitations.

Compared to existing studies based on ecological assessments of vegetation coverage and land use change [14,15,16], analyses of the impact of dams on river connectivity [20], or water quality evaluation based on WQI [10], this study integrated multi-source data (e.g., remote sensing data on aquatic space aggregation W4, per capita water resources statistics V2, and water quality monitoring data Q4). This approach systematically reveals multidimensional spatial heterogeneity: the upstream Qinghai Province has excellent water quality (Q = 0.21), but neighboring provinces face severe water shortage (Gansu: V = 0.06; Inner Mongolia: V = 0.06; Ningxia: V = 0.07). Midstream reaches of Shaanxi (W = 0.06) and Shanxi (W = 0.02) suffered from severs aquatic space fragmentation. The river network structural was quantified using the α, β, and γ indices, and it was found that Inner Mongolia (L = 0.36) had the highest water flow. The lower reaches of Shandong (O = 0.330) and Henan (O = 0.329) provinces exhibited superior aquatic biodiversity compared to the mid-upper regions.

Conventional linear models have serious flaws. For example, the DPSIR model [24] employs fixed weights and ignores dynamic interactions (e.g., nonlinear coupling between industrial wastewater treatment infrastructure and water quality compliance). The system dynamics models [25] assume that the system evolves gradually and ignore the sudden ecological changes caused by the fragmentation aquatic organisms. Fuzzy set evaluations [27] can handle uncertainty, but fail to quantify the impact of thresholds on system stability. In contrast, the catastrophe progression method can determine critical thresholds. For example, Gansu Province had a low score for water flow (L = 0.01), but its composite index dropped sharply (0.028) despite having moderate water quality (Q = 0.107) and a high number of aquatic biota (O = 0.126). This nonlinear response cannot be achieved in linear models such as the SBM model [26]. In contrast, the waters flow score in Inner Mongolia (L = 0.36) is close to the upper critical value of the swallowtail catastrophe model (0.4), indicating the improved connectivity may bring exponential ecological benefits. This mechanism allows the region to maintain a “good” overall rating (0.030) despite weak water volume (V = 0.063) and aquatic biota (O = 0.097).

6. Conclusions

Based on the multidimensional framework of “quality–quantity–space–flow–biota”, this study constructed a comprehensive water environment evaluation system with 19 indicators, and used the entropy–catastrophe progression method to systematically diagnose the water environment conditions in nine provinces and regions in the Yellow River basin, revealing the complex impact mechanisms of human activities on the water environment system. The main conclusions are as follows:

• The five-dimensional framework integrates water quality (four indicators), water quantity (five indicators), aquatic space (five indicators), water flow (three indicators), and aquatic biota (two indicators), comprehensively depicting the complex interactions within the water environment system. The spatial heterogeneity results are closely related to the geographical characteristics of the Yellow River basin, such as the ecological barrier in the upstream regions, the soil erosion belt in the midstream regions, and the alluvial plain in the downstream regions. Compared with traditional linear models, the entropy–catastrophe progression method is more suitable for describing nonlinear mutation processes in water environment systems. When the indicator of a specific dimension is lower than the critical threshold, the comprehensive evaluation index will gradually decrease, verifying the application value of catastrophe theory.
• The spatial heterogeneity of the water environment in the Yellow River basin is significant. Provinces with an “excellent” comprehensive evaluation grade, such as Qinghai Province (0.041) and Shandong Province (0.049), are located in the source area and estuary area of the Yellow River basin, respectively; while the midstream provinces, such as Shanxi Province (0.020) and Shaanxi Province (0.024), are key governance provinces due to deficiencies in multiple dimensions such as water quantity, water quality, and aquatic space.
• For provinces with a “poor” comprehensive water environment assessment, it is recommended to adopt differentiated governance measures: (1) Gansu and Ningxia should focus on promoting water-saving irrigation and virtual water trade. (2) Shaanxi and Shanxi should focus on promoting the deep treatment of industrial wastewater and the control of non-point source pollution. (3) Establish a three-level water space management system of “red line-buffer zone-restoration area” for the entire river basin.